配电网电容器优化的研究_电工文摘

配电网电容器优化的研究

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3.4混合算法
　　近年来也有一些研究者致力于将传统数学规划法和智能法相结合的研究。文献[27]将也采用类似的将ANN与动态规划相结合的方法来解决电容器的投切问题。这个方法分三步实施。首先，收集历史负荷数据，应用动态规划法离线确定最佳决策。其次，用ANN 的两种归类算法对负荷曲线归类，在每一类中，将前一步求出的最佳决策取平均得到预调度表。最后，固定预调度表中可信度高的决策，再用动态规划法优化可信度低的决策，得到最终的控制方案。前两步离线进行，后一步在线应用。在线应用时由于状态变量数大大下降，计算速度大大加快了。ANN技术用于控制的特点是在线计算快，特别适于实时控制。ANN离线训练时间长并不是主要缺点，问题的关键在于训练样本的获取。由于配电网络负荷变化频繁，对应每一种负荷模式都需要大量的样本来训练ANN，这限制了其实用性。
　　Miu K N和Chiang H D ^[28]研究了GA在三相不平衡配电网电容器优化配置及控制问题中的应用，构造了两级优化模型。一级优化用遗传算法确定一个可行解空间，二级优化采用基于灵敏度分析的启发式算法，用上一级所得到的可行解空间作为搜索的初值继续寻优。该方法花费的时间比单纯使用GA要少，但解的精度有所降低。
　　文献[29]提出改进的GA/TS混合算法并用于配电网电容器的实时投切，用GA求解初值，然后用TS求出最优解。
　　陈星莺等^[30]从经济运行的角度出发，以网损最小为目标函数建立了配电网无功优化控制问题，数学约束条件主要强调电容器投切次数的限制，采用模糊动态规划法计算配电网电容器的优化投切问题。混合算法可以将方法各自的优点充分发挥出来，而避开其缺点，从这一点来说，这类算法很有发展前途，但是如何对各种算法“取长补短”，发挥各自优势，仍需要进一步研究。
4结论
　　配电网电容器优化配置和投切问题大规模非线性组合优化问题。利用传统数学优化理论，方法成熟，收敛性好，可以从理论上得到全局最优解，但是这些方法大都要求目标函数连续可微，因此在应用时需要做某些简化假设，这会使得优化结果和实际情况不符，从而限制了其应用范围。基于ANN的算法可以在很短的时间内得出结果，但其精度取决于样本，而要获得完整的样本较困难，而且需要较长的时间训练样本。基于SA的算法从理论上可以获得全局最优解，但存在算法依赖参数和计算量大的缺点，可以应用于对计算速度要求不高的电容器优化配置及计划投切。GA虽然具备全局寻优能力，但其收敛性和计算量大限制了其应用。基于TS的算法由于列表的大小不易确定，过大或过小的列表都会影响TS的全局寻优能力。模糊数学和专家系统方法必须依赖于其他技术的发展。
　　综上可知，各种方法都有其优缺点，对于配电网络电容器优化配置及计划投切问题，对计算速度要求不是很苛刻，可以牺牲计算速度以获得高精度解；而电容器实时投切对算法的计算速度提出了更高要求，如何提高算法的计算速度同时也使得优化结果足够满意仍需开展进一步研究工作。

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